Implicit Bias Predicts Liking of Ingroup Members Who Are Comfortable With Intergroup Interaction.

We test a novel framework for how ingroup members are perceived during intergroup interaction. Across three experiments, we found that, above and beyond egalitarian attitudes and motivations, White observers' automatic responses to Blacks (i.e., their implicit anti-Black bias) shaped their affiliation toward ingroup targets who appeared comfortable engaging in interracial versus same-race interaction. White observers' implicit anti-Black bias negatively correlated with liking of White targets who were comfortable with Blacks (Experiments 1-3). The relationship between implicit bias and liking varied as a function of targets' nonverbal comfort in interracial interactions (Experiment 1). Specifically, implicit bias negatively correlated with liking of targets when targets' nonverbal behaviors revealed observers felt comfortable with interracial contact, irrespective of the nature of those behaviors (Experiment 2). Finally, the relationship between implicit bias and target liking was mediated by perceived similarity (Experiment 3). Theoretical implications for stigma-by-association, social network homogeneity, and extended contact are discussed

Graphic cigarette pack warnings do not produce more negative implicit evaluations of smoking compared to text-only warnings

Graphic warnings (GWs) on cigarette packs are widely used internationally with the aim of reducing smoking behavior. In the current study, we investigated whether GWs influence implicit evaluations of smoking, a potential moderator of smoking behavior, as measured with an Implicit Association Test (IAT). Results showed that viewing a GW did not produce more negative implicit evaluations of smoking for daily smokers, occasional smokers, or non-smokers, compared to viewing a text-only warning. If anything, effects were in the direction of evaluations of smoking being more positive after certain participants (i.e., daily and occasional smokers) viewed a GW. We also did not find any beneficial effects of GWs on explicit evaluations of smoking. These results contrast with the observation that non-smokers and occasional smokers (but not daily smokers) believed that GWs would be more effective than the text-only warnings. We discuss implications and limitations of these findings and provide recommendations for improving the effectiveness of cigarette pack warnings on implicit evaluations

Implicit Measures of Attitudes and Political Voting Behavior

Implicit measures have contributed to the prediction of behavior in numerous domains including the political realm. Some theoretical arguments suggest that implicit measures are unlikely to substantially improve the prediction of political voting behavior. Other arguments are more optimistic, especially regarding the prediction of undecided voters' behavior. Here, we review the evidence regarding the extent to which implicit measures improve the prediction of political voting behavior beyond explicit self-report measures. Results reveal that implicit measures are often statistically significant predictors. However, the inclusion of an implicit measure leads to modest or even no improvement of the overall accuracy of the original prediction. We conclude that implicit measures are likely to be practically relevant for predicting voting behavior only if researchers can identify new approaches. Related findings in political psychology may pave the way as they demonstrate that implicit measures can contribute unique knowledge not accounted for in other ways

The impact of instruction- and experience-based evaluative learning on IAT performance : a quad model perspective

Learning procedures such as mere exposure, evaluative conditioning, and approach/avoidance training have been used to establish evaluative responses as measured by the Implicit Association Test (IAT). In this paper, we used the Quad model to disentangle the processes driving IAT responses instantiated by these evaluative learning procedures. Half of the participants experienced one of these three procedures whereas the other half only received instructions about how the procedure would work. Across three experiments (total n = 4231), we examined the extent to which instruction-based versus experience-based evaluative learning impacted Quad estimates of the Activation of evaluative information in IAT responses. Relative to a control condition, both instruction- and experience-based evaluative learning procedures influenced Activation. Moreover, and contrary to what prevailing models of implicit evaluations would predict, in no instance did experience-based procedures influence (positive or negative) Activation more strongly than instruction-based procedures. This was true for analyses which combined procedures and also when testing all three procedures individually. Implications for the processes that mediate evaluative learning effects and the conditions under which those processes operate are discussed

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size

Classification accuracy of structural and functional connectomes across different depressive phenotypes

Phenotyping of major depressive disorder (MDD) in research can vary from study to study, which, together with heterogeneity of the disorder, may contribute to the inconsistent associations with various risk factors including neuroimaging features. These aspects also potentially underlie previous problems with machine learning methods using imaging data to inform predictive biomarkers. In this study we therefore aimed to examine the classification accuracy of structural and functional connectomes across different depressive phenotypes, including separating MDD subgroups into those with and without early childhood adversity (one of the largest risk factors for MDD associated with brain development). We applied logistic ridge regression to classify control and MDD participants defined according to six different MDD definitions in a large community-based sample (N = 14, 507). We used brain connectomic data based on six structural and two functional network weightings and conducted a comprehensive analysis to (i) explore how well different connectome modalities predict different MDD phenotypes commonly used in research, (ii) investigate whether stratification of MDD based on the presence or absence of early childhood adversity (measured with the childhood trauma questionnaire) can improve prediction accuracies, and (iii) identify important predictive features that are consistent across MDD phenotypes. We find that functional connectomes consistently outperform structural connectomes as features for MDD classification across phenotypes. Highest accuracy of 61.06% (chance level 50.0%) was achieved when predicting the Currently Depressed phenotype (i.e. the phenotype defined by the presence of more than five symptoms of depression in the past two weeks) with features based on partial correlation functional connectomes. Accuracy of classifying Currently Depressed participants with added CTQ threshold criterion rose to 65.74%. Application of the Jaccard index to assess predictive feature overlap indicated that there were neurobiological differences between MDD patients with and without childhood adversity. Further to that, analysis of predictive features for different MDD phenotypes with binomial tests revealed sensorimotor and visual functional subnetworks as consistently important for prediction. Our results provide the basis for future research, and indicate that differences in sensorimotor and visual subnetworks may serve as important biomarkers of MDD